Fungal-clocks: Ophio v. Beau
Biplabendu Das
24 February, 2022
# SAMPLE NAME
## specify sample name
sample.name <- c("beau", "ophio_cflo", "ophio_kim")
## specify names for all ocflo samples
sampleName <- c("ophio_cflo","ophio_ophio-infected")
## color scheme for the samples
col.scheme <- c("#5A829F", "#AD212F", "black", "#5C2849")
# SCRIPT NAME
## specify the name of the script (folder) where figures will be saved
script.name <- "01_comparing_gene_exp_ophio_beau"
# eJTK OUTPUT
## Set GammaP threshold below which genes are classified as rhythmic
gamma.pval = 0.05
## Set false discovery rate for functional enrichment analyses
FDR = 5# LOAD DATABASES (TC7)
# 1. TC6_ejtk.db
# Desc: This database contains all ejtk-output for TC6
ejtk.db <- dbConnect(RSQLite::SQLite(), paste0(path_to_repo,"/data/databases/TC6_fungal_ejtk.db"))
# which tables are in the database
src_dbi(ejtk.db)## src: sqlite 3.29.0 [/Users/biplabendudas/Documents/GitHub/Das_et_al_2022a/data/databases/TC6_fungal_ejtk.db]
## tbls: beau_rhythmic_genes_12h, beau_rhythmic_genes_24h, beau_zscores_08h,
## beau_zscores_12h, beau_zscores_24h, ophio_cflo_rhythmic_genes_12h,
## ophio_cflo_rhythmic_genes_24h, ophio_cflo_zscores_08h,
## ophio_cflo_zscores_12h, ophio_cflo_zscores_24h, ophio_kim_DD_zscores_24h,
## ophio_kim_LD_rhythmic_genes_24h, ophio_kim_LD_zscores_24h#
# 2. TC6_data.db
data.db <- dbConnect(RSQLite::SQLite(), paste0(path_to_repo,"/data/databases/TC6_fungal_data.db"))
src_dbi(data.db)## src: sqlite 3.29.0 [/Users/biplabendudas/Documents/GitHub/Das_et_al_2022a/data/databases/TC6_fungal_data.db]
## tbls: beau_expressed_genes, beau_fpkm, beau_log2fpkm, beau_zscores,
## ophio_cflo_expressed_genes, ophio_cflo_fpkm, ophio_cflo_log2fpkm,
## ophio_cflo_zscores, ophio_kim_DD_expressed_genes, ophio_kim_DD_fpkm,
## ophio_kim_DD_log2fpkm, ophio_kim_DD_zscores, ophio_kim_expressed_genes,
## ophio_kim_fpkm, ophio_kim_log2fpkm, ophio_kim_zscores#Overview/Goals
1. General patterns of gene expression
# number of all genes
all.genes <- list()
for (i in 1:length(sample.name)) {
all.genes[[i]] <- tbl(data.db, paste0(sample.name[[i]] ,"_fpkm")) %>%
collect()
writeLines(paste("Number of genes in", sample.name[[i]], ":", nrow(all.genes[[i]])))
}## Number of genes in beau : 10364
## Number of genes in ophio_cflo : 7455
## Number of genes in ophio_kim : 8577# A1: genes that have NO expression (FPKM == 0 at all time points)
not.expressed <- list()
for (i in 1:length(sample.name)) {
not.expressed[[i]] <-
tbl(data.db, paste0(sample.name[[i]] ,"_fpkm")) %>%
collect() %>%
filter_at(vars(starts_with("Z")), all_vars(. == 0)) %>%
pull(gene_name)
# How many genes are not expressed?
writeLines(paste("n(genes-NOT-EXPRESSED) in", sample.name[[i]], ":", length(not.expressed[[i]])))
}## n(genes-NOT-EXPRESSED) in beau : 759
## n(genes-NOT-EXPRESSED) in ophio_cflo : 190
## n(genes-NOT-EXPRESSED) in ophio_kim : 111# A2: run enrichment (make plot of enrichment found of non-expressed genes)
for (i in 1:length(sample.name)) {
writeLines(paste("running GO enrichment for NOT-EXPRESSED genes in", sample.name[[i]]))
# run enrichment
not.expressed[[i]] %>%
check_enrichment(.,
org = sample.name[[i]],
what = "pfams",
function.dir = path_to_repo,
bg = 'all') %>%
# # pull gene names for a given GO term
# separate_rows(., gene_name, sep = ", ") %>%
# filter(GO == "GO:0009405") %>% # pathogenesis
# # filter(GO == "GO:0090729") %>% # toxin activity
# # filter(GO == "GO:0044419") %>% # interspecies interaction between organisms
# # filter(GO == "GO:0020037") %>% # heme binding
# pull()
# go_enrichment_plot(clean = "no",
# function.dir = path_to_repo) %>%
print()
}## running GO enrichment for NOT-EXPRESSED genes in beau
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 16 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 no_annot no_desc 0 0.584 0.275 2854 BBA_00003, BBA…
## 2 PF05699 Dimer_Tnp_… 0 0.016 0.002 23 BBA_03709, BBA…
## 3 PF13857 Ank_5 0.00011 0.014 0.004 38 BBA_00617, BBA…
## 4 PF00078 RVT_1 0.000300 0.008 0.001 15 BBA_09633, BBA…
## 5 PF13391 HNH_2 0.000300 0.007 0.001 11 BBA_07949, BBA…
## 6 PF13606 Ank_3 0.00048 0.014 0.004 46 BBA_00617, BBA…
## 7 PF12796 Ank_2 0.00059 0.018 0.007 69 BBA_00617, BBA…
## 8 PF00023 Ank 0.00071 0.017 0.006 64 BBA_00617, BBA…
## 9 PF13637 Ank_4 0.00071 0.017 0.006 64 BBA_00617, BBA…
## 10 PF06985 HET 0.0176 0.007 0.002 23 BBA_00683, BBA…
## 11 PF11807 DUF3328 0.0254 0.007 0.002 25 BBA_00019, BBA…
## 12 PF00082 Peptidase_… 0.0304 0.008 0.003 34 BBA_00619, BBA…
## 13 PF05729 NACHT 0.0304 0.007 0.003 27 BBA_00617, BBA…
## 14 PF00067 p450 0.0304 0.014 0.007 77 BBA_02747, BBA…
## 15 PF01565 FAD_bindin… 0.0391 0.007 0.003 29 BBA_00709, BBA…
## 16 PF13847 Methyltran… 0.0391 0.008 0.004 37 BBA_09438, BBA…
## running GO enrichment for NOT-EXPRESSED genes in ophio_cflo
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 3 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 no_annot no_desc 0 0.63 0.421 1992 GQ602_000673, G…
## 2 PF00067 p450 0.00048 0.045 0.011 51 GQ602_001415, G…
## 3 PF01375 Enterotoxi… 0.00104 0.032 0.007 34 GQ602_002072, G…
## running GO enrichment for NOT-EXPRESSED genes in ophio_kim
## [1] "Loading annotation file for Ophiocordyceps kimflemingae"
## [1] "Done."
## [1] "There are no pfams terms to test enrichment for."# B: genes that are expressed (FPKM > 1 for at least one time point)
expressed <- list()
for (i in 1:length(sample.name)) {
expressed[[i]] <-
tbl(data.db, paste0(sample.name[[i]],"_expressed_genes")) %>%
filter(expressed=="yes") %>%
collect() %>%
pull(gene_name)
# How many genes are expressed?
writeLines(paste("n(EXPRESSED) in", sample.name[[i]], ":", length(expressed[[i]])))
}## n(EXPRESSED) in beau : 9006
## n(EXPRESSED) in ophio_cflo : 6998
## n(EXPRESSED) in ophio_kim : 81502. Daily rhythms in gene expression
## Load all the rhythmic genesets
## Note, ordered according to their p-value; highly rhythmic at the top.
#
# Choose period
period = '24'
##
rhy <- list()
for (i in 1:2) {
rhy[[i]] <-
tbl(ejtk.db, paste0(sample.name[[i]],"_zscores_",period,'h')) %>%
filter(GammaP < gamma.pval) %>%
select(ID, GammaP) %>% collect() %>% arrange(GammaP) %>%
select(ID) %>% pull()
# How many genes are rythmic?
writeLines(paste0("n(rhythmic-",period, "h) in ", sample.name[[i]], " : ", length(rhy[[i]])))
}## n(rhythmic-24h) in beau : 1872
## n(rhythmic-24h) in ophio_cflo : 2285Hierarchical clustering of rhy24
- perform hierarchical clustering of 24h-rhythmic genes into four clusters;
- plot time-course heatmaps for the clustered 24h-rhythmic geneset
- Identify the day-peaking and night-peaking clusters visually.
## initialise lists to hold input and output of the hierarchical clustering
zscore.dat <- list() # zscore data (input)
my_gene_col <- list() # cluster identity for each rhythmic gene (output)
rhy.heat <- list() # pheatmap that can be saved/plotted (output)
# specify number of clusters
n_clusters <- 4
## run clustering and plot
for (i in 1:2) {
## load zscore dataset
zscore.dat[[i]] <- data.db %>% tbl(., paste0(sample.name[[i]],"_zscores")) %>% collect()
# Filter the zscores to keep only rhythmic genes
zscore.rhy <-
zscore.dat[[i]] %>%
filter(gene_name %in% rhy[[i]]) %>%
as.data.frame()
# Set genes as rownames and convert it into a matrix
rownames(zscore.rhy) = zscore.rhy$gene_name
zscore.rhy <- as.matrix(zscore.rhy[-1])
# Hierarchical clustering of the genesets
my_hclust_gene <- hclust(dist(zscore.rhy), method = "complete")
# Make annotations for the heatmaps
my_clusters <- cutree(tree = as.dendrogram(my_hclust_gene), k = n_clusters) # k= clusters
my_gene_col[[i]] <- data.frame(cluster = my_clusters)
# I’ll add some column annotations and create the heatmap.
# Annotations for:
# 1. Is the sample collected during the light or dark phase?
my_sample_col <- data.frame(phase = rep(c("light", "dark", "light"), c(5,6,1)))
row.names(my_sample_col) <- colnames(zscore.rhy)
# Manual color palette
my_colour = list(
phase = c(light = "#F2E18D", dark = "#010440"),
cluster = viridis::cividis(100)[c(seq(10,80,by=round(80/(n_clusters), 0)))])
# Color scale
my.breaks = seq(min(zscore.rhy), max(zscore.rhy), by=0.1)
# my.breaks = seq(min(zscore.rhy), max(zscore.rhy), by=0.06)
# Let's plot!
rhy.heat[[i]] <-
pheatmap(zscore.rhy, show_rownames = F, show_colnames = F,
annotation_row = my_gene_col[[i]],
annotation_col = my_sample_col,
cutree_rows = n_clusters, # OG was 4
# cutree_cols = 2,
annotation_colors = my_colour,
border_color=FALSE,
cluster_cols = F,
breaks = my.breaks,
## color scheme borrowed from:
color = inferno(length(my.breaks) - 1),
treeheight_row = 0,
# treeheight_col = 0,
# remove the color scale or not
main = paste0(sample.name[[i]], " 24h-rhythmic \n (n=", nrow(zscore.rhy), " genes)"),
## annotation legend
annotation_legend = T,
## Color scale
legend = T)
}
Phase plots
rhy.24.sig <- list()
phase.ejtk <- list()
# Obtain the phases of 24h-rhythmic genes beau v. ophio_cflo
for (i in 1:2) {
rhy.24.sig[[i]] <-
tbl(ejtk.db, paste0(sample.name[i],"_zscores_24h")) %>%
filter(GammaP < gamma.pval) %>%
collect()
# Get the phases of the best matched waveforms
phase.ejtk[[i]] <- circular::circular(rhy.24.sig[[i]]$Phase, units="hours", template="clock24")
# # Get the time-of-day of expression peak
# phase.ejtk[[i]] <- circular::circular(rhy.24.sig[[i]]$MaxLoc, units="hours", template="clock24")
# # Get the time-of-day of expression trough
# phase.ejtk[[i]] <- circular::circular(rhy.24.sig[[i]]$MinLoc, units="hours", template="clock24")
}
# save all the circular phases in a list
l.phases <- phase.ejtk
# let's name the list elements for later use and reference
names(l.phases) <- sample.name[1:2]
writeLines("Performing Watson test to check if the average peak of 24h-rhythms in Beau and Ophio-cflo differs significantly")## Performing Watson test to check if the average peak of 24h-rhythms in Beau and Ophio-cflo differs significantly# For all rhy genes
beau.ophio <- watson.two.test(l.phases[[1]],l.phases[[2]], alpha = FDR/100)
writeLines("Beau v. Ophio-cflo")## Beau v. Ophio-cflobeau.ophio %>% print()##
## Watson's Two-Sample Test of Homogeneity
##
## Test Statistic: 5.9634
## Level 0.05 Critical Value: 0.187
## Reject Null Hypothesis## Plot the phase distributions
# Initialize a list for saving the ggplots
g <- list()
means <- as.numeric(lapply(phase.ejtk, mean))
means <- circular(means, units="hours", template="clock24")
for(i in 1:length(l.phases)) {
# define phase levels
ordered_phases <- c("2","4","6","8","10","12",
"14","16","18","20","22","24")
df.test <- l.phases[[i]] %>%
as.data.frame() %>%
mutate(phase = x) %>%
mutate(phase = replace(phase, x=="0", "24")) %>%
select(-x) %>%
group_by(phase = factor(phase, levels = ordered_phases)) %>%
summarise(n_genes = n())
m <- as.numeric(means[i])
g[[i]] <-
ggplot(df.test, aes(x=factor(phase), y=n_genes)) +
# indicate light-dark phase
geom_rect(aes(xmin = as.factor(12), xmax = as.factor(24), ymin = -Inf, ymax = Inf),
fill = "grey80", alpha = 0.05, color=NA) +
geom_bar(stat='identity', fill=col.scheme[[i]]) +
xlab(c(names(l.phases)[i])) +
scale_y_continuous(breaks = c(0,300,600), limits = c(0,700)) +
coord_polar() +
theme_Publication(base_size = 20) +
theme(# axis.title.x=element_blank(),
# axis.text.x=element_blank(),
legend.position = "none")
#ggtitle(paste0("Dataset: ", names(l.phases)[i]))
}
ggpubr::ggarrange(plotlist=g,
nrow = 1, ncol = 2,
widths = c(1,1), labels = NA)
Clusters of rhythmic genes
- Which processes are identified clusters of rhythmic genes involved in?
- How do they fluctuate throughout the day?
Beau - 24h
for (i in 1:n_clusters){
writeLines(paste0("Species: ", sample.name[[1]], "\n", "24h-rhythmic genes, Cluster: ", i))
# Summary
genes <- my_gene_col[[1]] %>% rownames_to_column("g") %>% filter(cluster==as.character(i)) %>% pull(g)
writeLines(paste0("n(genes) = ", length(genes),"\n"))
# Enrichment
overrepresented.terms <-
genes %>%
check_enrichment(.,
function.dir = path_to_repo,
what = "pfams",
org = sample.name[[1]],
bg = expressed[[1]],
filter = T,
plot = F)
writeLines(paste0("\n", "n(overrepresented terms) = ", nrow(overrepresented.terms), "\n"))
print(overrepresented.terms %>% as_tibble())
# Stacked zplot
genes %>%
stacked.zplot_tc6(cond = "beau") %>%
multi.plot(rows = 1, cols = 1)
}## Species: beau
## 24h-rhythmic genes, Cluster: 1
## n(genes) = 767
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 28
##
## # A tibble: 28 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF03810 IBN_N 0 0.009 0.001 8 BBA_00780, BBA…
## 2 PF07714 Pkinase_Tyr 0 0.036 0.012 105 BBA_00171, BBA…
## 3 PF00069 Pkinase 0 0.037 0.014 124 BBA_00171, BBA…
## 4 PF02535 Zip 0.00008 0.007 0.001 8 BBA_01230, BBA…
## 5 PF00689 Cation_ATP… 0.00012 0.008 0.001 12 BBA_00548, BBA…
## 6 PF00512 HisKA 0.000130 0.007 0.001 9 BBA_00328, BBA…
## 7 PF05729 NACHT 0.000130 0.009 0.002 17 BBA_01725, BBA…
## 8 PF00072 Response_r… 0.000130 0.008 0.001 13 BBA_00328, BBA…
## 9 PF00690 Cation_ATP… 0.000130 0.008 0.001 13 BBA_00548, BBA…
## 10 PF04082 Fungal_tra… 0.00017 0.032 0.014 125 BBA_00237, BBA…
## # … with 18 more rows
## Species: beau
## 24h-rhythmic genes, Cluster: 2
## n(genes) = 550
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 2
##
## # A tibble: 2 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 "PF00226" DnaJ 0.00041 0.013 0.003 23 BBA_00135, BBA_0…
## 2 "" no_desc 0.00199 0.819 0.757 6815 BBA_00007, BBA_0…
## Species: beau
## 24h-rhythmic genes, Cluster: 3
## n(genes) = 337
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 2
##
## # A tibble: 2 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF08659 KR 0.0170 0.021 0.007 60 BBA_01241, BBA_…
## 2 PF13561 adh_short_… 0.0198 0.021 0.008 68 BBA_00446, BBA_…
## Species: beau
## 24h-rhythmic genes, Cluster: 4
## n(genes) = 218
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 2
##
## # A tibble: 2 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 "PF00076" RRM_1 0.00656 0.023 0.006 55 BBA_00301, BBA_0…
## 2 "" no_desc 0.00943 0.832 0.757 6815 BBA_00129, BBA_0…
Ophio-cflo - 24h
for (i in 1:n_clusters){
writeLines(paste0("Species: ", sample.name[[2]], "\n", "24h-rhythmic genes, Cluster: ", i))
# Summary
genes <- my_gene_col[[2]] %>% rownames_to_column("g") %>% filter(cluster==as.character(i)) %>% pull(g)
writeLines(paste0("n(genes) = ", length(genes),"\n"))
# Enrichment
overrepresented.terms <-
genes %>%
check_enrichment(.,
function.dir = path_to_repo,
what = "pfams",
org = sample.name[[2]],
bg = expressed[[2]],
filter = T,
plot = F)
writeLines(paste0("\n", "n(overrepresented terms) = ", nrow(overrepresented.terms), "\n"))
print(overrepresented.terms %>% as_tibble())
# Stacked zplot
stacked.plot1 <- genes %>% stacked.zplot_tc6(cond = sampleName[[1]]) %>% pluck(1)
stacked.plot2 <- genes %>% stacked.zplot_tc6(cond = sampleName[[2]]) %>% pluck(1)
ggpubr::ggarrange(plotlist=list(stacked.plot1, stacked.plot2),
nrow = 1, ncol = 2,
widths = c(1,1), labels = NA) %>%
print()
}## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 1
## n(genes) = 833
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 25
##
## # A tibble: 25 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF00512 HisKA 0.00003 0.01 0.002 7 GQ602_001137, …
## 2 PF00072 Response_r… 0.00008 0.012 0.002 10 GQ602_001137, …
## 3 PF00632 HECT 0.00008 0.009 0.001 6 GQ602_000948, …
## 4 PF00664 ABC_membra… 0.00038 0.014 0.003 15 GQ602_000447, …
## 5 PF14604 SH3_9 0.00066 0.012 0.003 13 GQ602_000765, …
## 6 PF07653 SH3_2 0.00259 0.01 0.003 12 GQ602_000765, …
## 7 PF00018 SH3_1 0.00337 0.012 0.004 16 GQ602_000765, …
## 8 PF02518 HATPase_c 0.00337 0.01 0.003 13 GQ602_001137, …
## 9 PF02801 Ketoacyl-s… 0.00337 0.01 0.003 13 GQ602_002162, …
## 10 PF00109 ketoacyl-s… 0.00498 0.01 0.003 14 GQ602_002162, …
## # … with 15 more rows
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 2
## n(genes) = 465
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 2
##
## # A tibble: 2 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF04082 Fungal_tra… 0.00094 0.039 0.013 54 GQ602_000498, G…
## 2 PF00172 Zn_clus 0.0468 0.036 0.017 75 GQ602_000498, G…
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 3
## n(genes) = 354
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 0
##
## # A tibble: 0 x 7
## # … with 7 variables: annot_term <chr>, annot_desc <chr>, adj_pVal <dbl>,
## # sam_freq <dbl>, back_freq <dbl>, n_annot_bg <int>, gene_name <chr>
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 4
## n(genes) = 633
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 4
##
## # A tibble: 4 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF00118 Cpn60_TCP1 0 0.024 0.002 10 GQ602_000364, GQ…
## 2 PF00076 RRM_1 0 0.058 0.014 60 GQ602_000255, GQ…
## 3 PF00004 AAA 0.00033 0.034 0.01 45 GQ602_000605, GQ…
## 4 PF00400 WD40 0.0168 0.041 0.021 91 GQ602_000228, GQ…
reducing redundant GO terms
Note to self: If we use GO terms instead of pfams, we will need to reduce the redundant GO annotations that is present in fungal annotation data.
Two options to do this:
Option #1: Get the list of overrepresented GO terms and their associated p-values and use REVIGO portal online to reduce the redundant terms
Option #2: Use the scripts provided by REVIGO to programmatically run REVIGO using bash/R. For more information see (here)[http://revigo.irb.hr/FAQ.aspx#q07] Status: tried running it via bash, and it didn’t work; NEED TO FIGURE IT OUT.
3. Beau v. Ophio - 24h-rhy
Next, we compare the homologous genes in both the fungi to understand if the rhythmic genes (and processes) in the two fungi are similar or not; also, is there any differences in the daily expression of these genes between the two fungal parasites?
Obtain homology data
# Read the source file
homology.file <- "ophio_beau_homology.csv"
homology.file <-
paste0(path_to_repo, "/results/proteinortho/", homology.file) %>%
read.csv(., stringsAsFactors = F, na.strings = c(" ","","NA"))
# Clean the source file to keep distinct gene-gene homologs
homology.dat <-
homology.file %>%
# names() %>%
select(ophio_gene, beau_gene) %>%
na.omit() %>%
distinct() %>%
group_by(beau_gene) %>%
filter(n()==1) %>%
select(beau_gene, ophio_gene)
writeLines(paste("Of the", length(expressed[[2]]), "genes expressed in Ophio-cflo,",
"and", length(expressed[[1]]), "genes expressed in Beau,\n",
nrow(homology.dat), "genes show one-to-one orthology"))## Of the 6998 genes expressed in Ophio-cflo, and 9006 genes expressed in Beau,
## 5274 genes show one-to-one orthologySummarize the results
for (i in 1:2){
# exp.dat <- expressed[[i]]
rhy.dat <- rhy[[i]]
ortho.dat <- homology.dat %>% pull(i)
listInput <- list(rhy.dat, ortho.dat)
names(listInput) <- c(paste0(sample.name[[i]], c("_rhy24","_ortho")))
library(UpSetR)
library(viridis)
# caste.col <- c("#F23030","#1A80D9")
upset(fromList(listInput),
number.angles = 0, point.size = 3, line.size = 1.5,
mainbar.y.label = "Number of overlapping genes",
sets.x.label = "Sig. rhy genes",
text.scale = c(1.5, # y-axis label ("# overlapping genes")
2, # y-axis tick labels ("1000, 2000,..")
1.5, # label for histogram ("sig. rhy genes")
1, # tick labels for histogram
1.5, # set names ("Cflo-brain_08h,..")
1.5),
sets = names(listInput),
nintersects = 15,
keep.order = T,
sets.bar.color = viridis(1),
# adding queries
query.legend = "bottom"
) %>%
print()
writeLines(paste0(
"The above plot shows that of the ", length(rhy.dat), " rhythmic genes in ", sample.name[[i]],
", how many has an 1:1 ortholog in the other fungal species. \n",
"Note, orthologous genes were identified using proteinortho "))
}
## The above plot shows that of the 1872 rhythmic genes in beau, how many has an 1:1 ortholog in the other fungal species.
## Note, orthologous genes were identified using proteinortho
## The above plot shows that of the 2285 rhythmic genes in ophio_cflo, how many has an 1:1 ortholog in the other fungal species.
## Note, orthologous genes were identified using proteinorthoRhy24 genes w/ orthologs
Next, I wanted to see if certain gene clusters show a characteristic difference in their daily expression in a species-specific manner (meaning, same genes but different daily expression in the two fungal species?).
To do so, I performed hierarchical clustering of all orthologous genes that show sig. 24h rhythms in EITHER Ocflo OR Beau to identify clusters of genes that show a synchronized expression in each of the two fungal species.
STOPPED HERE!!!
rhy.homology.dat <-
homology.dat %>%
filter(beau_gene %in% rhy[[1]] | ophio_gene %in% rhy[[2]])Hierarchical clustering
### Make the dataframe for plotting
zscore.rhy.homology.dat <-
zscore.dat[[1]] %>%
filter(gene_name %in% rhy.homology.dat[[1]]) %>%
rename_at(vars(starts_with("ZT")), ~ (gsub("A", "B", .x, fixed = TRUE))) %>% # fix colnames for beau
# add ophio homologs for the beau genes
left_join(rhy.homology.dat, by=c("gene_name" = "beau_gene")) %>%
# remove the beau names and keep the ophio names only
select(-1) %>%
select(gene_name = ophio_gene, everything()) %>%
# join ophio-cflo data
left_join(zscore.dat[[2]], by="gene_name") %>%
# drop any genes without expression values (NA)
na.omit() %>%
as.data.frame() %>%
# set genes as rownames
column_to_rownames("gene_name")
# Set genes as rownames and convert it into a matrix
# rownames(zscore.rhy.homology.dat) = zscore.rhy.homology.dat$gene_name
zscore.rhy.homology.dat <- as.matrix(zscore.rhy.homology.dat)
# Hierarchical clustering of the genesets
my_hclust_gene <- hclust(dist(zscore.rhy.homology.dat), method = "complete")
# Make annotations for the heatmaps
n_clusters <- 4
my_clusters <- cutree(tree = as.dendrogram(my_hclust_gene), k = n_clusters) # k= clusters
my_gene_col <- data.frame(cluster = my_clusters)
# I’ll add some column annotations and create the heatmap.
# Annotations for:
# 1. Is the sample collected during the light or dark phase?
my_sample_col <- data.frame(phase = rep(rep(c("light", "dark", "light"),c(5,6,1)),2),
conds = rep(c("beau", "ophio_cflo"), each=12))
row.names(my_sample_col) <- colnames(zscore.rhy.homology.dat)
# Manual color palette
my_colour = list(
phase = c(light = "#F2E18D", dark = "#010440"),
conds = c(beau = "#5A829F", ophio_cflo = "#AD212F"),
cluster = viridis::cividis(100)[c(seq(10,80,by=round(80/(n_clusters), 0)))])
# Color scale
my.breaks = seq(min(zscore.rhy.homology.dat), max(zscore.rhy.homology.dat), by=0.1)
# my.breaks = seq(min(zscore.rhy), max(zscore.rhy), by=0.06)
# Let's plot!
pheatmap(zscore.rhy.homology.dat, show_rownames = F, show_colnames = F,
annotation_row = my_gene_col,
annotation_col = my_sample_col,
cutree_rows = n_clusters, # OG was 4
# cutree_cols = 4,
gaps_col = c(12,24),
annotation_colors = my_colour,
border_color=FALSE,
cluster_cols = F,
breaks = my.breaks,
## color scheme borrowed from:
color = inferno(length(my.breaks) - 1),
treeheight_row = 0,
# treeheight_col = 0,
# remove the color scale or not
main = paste0("24h-rhythmic in Ocflo or Beau \n (n=",
nrow(zscore.rhy.homology.dat), " orthologous genes)"),
## annotation legend
annotation_legend = T,
## Color scale
legend = T)
Individual clusters
sampleName <- c("ophio_cflo","ophio_ophio-infected")
for (j in 1:2) {
for (i in 1:n_clusters){
writeLines(paste0("Species: ", sample.name[[j]], "\n", "24h-rhythmic genes, Cluster: ", i))
# Summary
genes <- my_gene_col %>% rownames_to_column("g") %>% filter(cluster==as.character(i)) %>% pull(g)
writeLines(paste0("n(genes) = ", length(genes),"\n"))
# define the background geneset for enrichment analysis
bg.genes <- homology.dat %>% pull(ophio_gene) %>% unique()
## Transform gene names (ophio -> beau) and refine background geneset
if (j == 1) {
genes <-
homology.dat %>%
filter(ophio_gene %in% genes) %>%
pull(beau_gene)
bg.genes <- homology.dat %>% pull(beau_gene) %>% unique()
}
# Enrichment
overrepresented.terms <-
genes %>%
check_enrichment(.,
function.dir = path_to_repo,
what = "pfams",
org = sample.name[[j]],
bg = expressed[[j]],
filter = T,
plot = F)
print(overrepresented.terms)
writeLines(paste0("\n", "n(overrepresented terms) = ", nrow(overrepresented.terms), "\n"))
# Stacked zplot
if (j==2) {
# Stacked zplot
stacked.plot1 <- genes %>% stacked.zplot_tc6(cond = sampleName[[1]]) %>% pluck(1)
stacked.plot2 <- genes %>% stacked.zplot_tc6(cond = sampleName[[2]]) %>% pluck(1)
ggpubr::ggarrange(plotlist=list(stacked.plot1, stacked.plot2),
nrow = 1, ncol = 2,
widths = c(1,1), labels = NA) %>%
print()
} else {
genes %>%
stacked.zplot_tc6(cond = sample.name[[j]]) %>%
multi.plot(rows = 1, cols = 1)
}
}
}## Species: beau
## 24h-rhythmic genes, Cluster: 1
## n(genes) = 718
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 7 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 "PF01248" Ribosomal_… 0 0.007 0.001 5 BBA_01098, BBA_…
## 2 "" no_desc 0 0.893 0.757 6815 BBA_00007, BBA_…
## 3 "PF00118" Cpn60_TCP1 0 0.01 0.001 10 BBA_00188, BBA_…
## 4 "PF00226" DnaJ 0.00016 0.011 0.003 23 BBA_00157, BBA_…
## 5 "PF01423" LSM 0.00042 0.008 0.002 16 BBA_00156, BBA_…
## 6 "PF00076" RRM_1 0.00103 0.017 0.006 55 BBA_00301, BBA_…
## 7 "PF00227" Proteasome 0.00108 0.007 0.002 14 BBA_01972, BBA_…
##
## n(overrepresented terms) = 7
## Species: beau
## 24h-rhythmic genes, Cluster: 2
## n(genes) = 1026
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 31 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 "" no_desc 0 0.844 0.757 6815 BBA_00011, BBA…
## 2 "PF07714" Pkinase_Tyr 0 0.035 0.012 105 BBA_00011, BBA…
## 3 "PF00069" Pkinase 0 0.035 0.014 124 BBA_00011, BBA…
## 4 "PF03810" IBN_N 0 0.007 0.001 8 BBA_00780, BBA…
## 5 "PF00454" PI3_PI4_ki… 0.00002 0.006 0.001 8 BBA_02054, BBA…
## 6 "PF13855" LRR_8 0.00002 0.005 0.001 6 BBA_00407, BBA…
## 7 "PF00168" C2 0.00002 0.007 0.001 11 BBA_00011, BBA…
## 8 "PF00018" SH3_1 0.000150 0.009 0.002 20 BBA_00388, BBA…
## 9 "PF14604" SH3_9 0.00019 0.008 0.002 17 BBA_00388, BBA…
## 10 "PF00176" SNF2_N 0.0002 0.009 0.002 21 BBA_00577, BBA…
## # … with 21 more rows
##
## n(overrepresented terms) = 31
## Species: beau
## 24h-rhythmic genes, Cluster: 3
## n(genes) = 451
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 1 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 "" no_desc 0 0.852 0.757 6815 BBA_00012, BBA_0…
##
## n(overrepresented terms) = 1
## Species: beau
## 24h-rhythmic genes, Cluster: 4
## n(genes) = 324
##
## [1] "Loading annotation file for Beauveria bassiana"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 8 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 "" no_desc 0 0.898 0.757 6815 BBA_00042, BBA_0…
## 2 "PF00004" AAA 0.000070 0.028 0.006 50 BBA_00380, BBA_0…
## 3 "PF00025" Arf 0.00248 0.015 0.003 31 BBA_01574, BBA_0…
## 4 "PF08477" Roc 0.00248 0.015 0.003 31 BBA_01574, BBA_0…
## 5 "PF00071" Ras 0.00248 0.015 0.004 33 BBA_01574, BBA_0…
## 6 "PF01926" MMR_HSR1 0.00248 0.015 0.004 33 BBA_01574, BBA_0…
## 7 "PF00076" RRM_1 0.00773 0.019 0.006 55 BBA_00117, BBA_0…
## 8 "PF08659" KR 0.0438 0.015 0.007 60 BBA_01241, BBA_0…
##
## n(overrepresented terms) = 8
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 1
## n(genes) = 718
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 10 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF01248 Ribosomal_… 0 0.017 0.001 5 GQ602_001149, …
## 2 PF00118 Cpn60_TCP1 0 0.024 0.002 10 GQ602_000364, …
## 3 PF00226 DnaJ 0.00005 0.027 0.005 23 GQ602_000387, …
## 4 PF00076 RRM_1 0.000140 0.044 0.014 60 GQ602_000296, …
## 5 PF01423 LSM 0.000140 0.02 0.004 16 GQ602_000162, …
## 6 PF00227 Proteasome 0.00048 0.017 0.003 14 GQ602_000370, …
## 7 PF00153 Mito_carr 0.0225 0.02 0.008 35 GQ602_001581, …
## 8 PF13649 Methyltran… 0.0273 0.02 0.009 37 GQ602_001375, …
## 9 PF08241 Methyltran… 0.0283 0.02 0.009 38 GQ602_001375, …
## 10 PF00400 WD40 0.0487 0.037 0.021 91 GQ602_000262, …
##
## n(overrepresented terms) = 10
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 2
## n(genes) = 1026
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 34 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF07714 Pkinase_Tyr 0 0.058 0.02 86 GQ602_000284, …
## 2 PF00069 Pkinase 0 0.058 0.021 90 GQ602_000284, …
## 3 PF00664 ABC_membra… 0.00001 0.015 0.003 15 GQ602_000061, …
## 4 PF00168 C2 0.00002 0.012 0.003 11 GQ602_000284, …
## 5 PF00172 Zn_clus 0.00003 0.041 0.017 75 GQ602_000498, …
## 6 PF00454 PI3_PI4_ki… 0.0001 0.009 0.002 8 GQ602_001647, …
## 7 PF03810 IBN_N 0.0001 0.009 0.002 8 GQ602_002695, …
## 8 PF14531 Kinase-like 0.00029 0.012 0.003 14 GQ602_000284, …
## 9 PF00512 HisKA 0.0004 0.008 0.002 7 GQ602_001137, …
## 10 PF03372 Exo_endo_p… 0.0004 0.008 0.002 7 GQ602_002242, …
## # … with 24 more rows
##
## n(overrepresented terms) = 34
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 3
## n(genes) = 451
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 4 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF07690 MFS_1 0.0113 0.052 0.022 95 GQ602_000016, G…
## 2 PF00083 Sugar_tr 0.0474 0.028 0.013 55 GQ602_000016, G…
## 3 PF13450 NAD_bindin… 0.0474 0.02 0.008 36 GQ602_000482, G…
## 4 PF00400 WD40 0.0474 0.04 0.021 91 GQ602_000116, G…
##
## n(overrepresented terms) = 4
## Species: ophio_cflo
## 24h-rhythmic genes, Cluster: 4
## n(genes) = 324
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
## # A tibble: 8 x 7
## annot_term annot_desc adj_pVal sam_freq back_freq n_annot_bg gene_name
## <chr> <chr> <dbl> <dbl> <dbl> <int> <chr>
## 1 PF00004 AAA 0.0001 0.052 0.01 45 GQ602_001710, GQ…
## 2 PF08477 Roc 0.00595 0.029 0.007 31 GQ602_001942, GQ…
## 3 PF00025 Arf 0.00595 0.029 0.008 33 GQ602_001942, GQ…
## 4 PF00071 Ras 0.00595 0.029 0.008 33 GQ602_001942, GQ…
## 5 PF01926 MMR_HSR1 0.0119 0.029 0.009 39 GQ602_001353, GQ…
## 6 PF00076 RRM_1 0.0227 0.035 0.014 60 GQ602_000255, GQ…
## 7 PF08659 KR 0.0227 0.029 0.011 47 GQ602_002426, GQ…
## 8 PF00106 adh_short 0.0299 0.029 0.012 51 GQ602_002426, GQ…
##
## n(overrepresented terms) = 8
Overlap b/n clusters and rhy24
## Visualize the overlap
cluster.dat <- list()
for (i in 1:n_clusters) {
cluster.dat[[i]] <- my_gene_col %>%
rownames_to_column("g") %>% filter(cluster==as.character(i)) %>% pull(g)
}
names(cluster.dat) <- paste0("Cluster_",1:4)
for (j in 1:2) {
rhy.dat <- rhy[[j]]
cluster.dat.dummy <- cluster.dat
if (j == 1) {
for (i in 1:n_clusters) {
cluster.dat.dummy[[i]] <-
homology.dat %>%
filter(ophio_gene %in% cluster.dat.dummy[[i]]) %>%
pull(beau_gene)
}
}
listInput <- list(rhy.dat,
cluster.dat.dummy[[1]], cluster.dat.dummy[[2]],
cluster.dat.dummy[[3]], cluster.dat.dummy[[4]])
names(listInput) <- c(paste0(sample.name[[j]], c("_rhy24")), paste0("cluster_",1:4))
library(UpSetR)
library(viridis)
# caste.col <- c("#F23030","#1A80D9")
upset(fromList(listInput),
number.angles = 0, point.size = 3, line.size = 1.5,
mainbar.y.label = "Number of overlapping genes",
sets.x.label = "Sig. rhy genes",
text.scale = c(1.5, # y-axis label ("# overlapping genes")
2, # y-axis tick labels ("1000, 2000,..")
1.5, # label for histogram ("sig. rhy genes")
1, # tick labels for histogram
1.5, # set names ("Cflo-brain_08h,..")
1.5),
sets = names(listInput),
nintersects = 15,
keep.order = T,
sets.bar.color = viridis(1),
# adding queries
query.legend = "bottom"
) %>%
print()
}
It seems that majority of the genes in Cluster 3 and 4 are sig. rhythmic in Ophio but not in Beau. We will perform the pairwise Fisher’s exact test to find out. Let’s dig in!
NOTE: We need to think about the best way to perform the Fisher’s exact test. For starters, I am transforming all the gene names to Ophio
# LIST ONE - Cluster identity
list1 <- cluster.dat
names(list1) <- names(cluster.dat)
## LIST TWO - ophio rhythmic genes
beau.ortho.rhy <- homology.dat %>% filter(beau_gene %in% rhy[[1]]) %>% pull(ophio_gene) %>% unique()
ocflo.ortho.rhy <- homology.dat %>% filter(ophio_gene %in% rhy[[2]]) %>% pull(ophio_gene) %>% unique()
list2 <- list(beau.ortho.rhy, ocflo.ortho.rhy)
names(list2) <- paste0(sample.name[1:2], c("_24h"))
## CHECK FOR OVERLAP
library(GeneOverlap)
# define the background geneset
# in our case, it would be the number of orthologous genes between beau and Ophio_cflo
nGenes = homology.dat %>% pull(ophio_gene) %>% unique() %>% length()
## make a GOM object
gom.1v2 <- newGOM(list1, list2, genome.size = nGenes)
png(paste0(path_to_repo, "/results/figures/BD/ocflo_beau_orthologs_rhy_overlap.png"),
width = 15, height = 15, units = "cm", res = 300)
drawHeatmap(gom.1v2,
adj.p=T,
cutoff=0.01,
what="odds.ratio",
# what="Jaccard",
log.scale = T,
note.col = "grey60")
trash <- dev.off()
Orthologous rhy24 genes
As we predicted, Cluster 3 and 4 genes show a stronger overlap with 24h-rhythmic genes in O. cflo as comapred to Beauveria (as can be seen from both log2-odds ratio and the associated q-value). The signal is strongest for Cluster 4, so let’s see which genes are in this cluster.
Cluster-4 genes
## Get the annotation data
ocflo.annots <- read.csv(paste0(path_to_repo, "/data/ophio_cflo_TC6_data.csv"), stringsAsFactors = F) %>% as.tibble()
ocflo.annots %<>%
filter(expressed=="yes") %>%
select(gene_name = gene_ID_ncbi, gene_ID_robin, blast_annot, GammaP_24h, GOs:ophio_kim_homolog)
## Subset the gene_names
ocflo.annots %>%
filter(gene_name %in% cluster.dat[[4]]) %>%
# plot the q-values
ggplot() +
geom_density(aes(x=GammaP_24h)) +
labs(x="rhythmicity, 24h (q-value)") +
scale_x_continuous(breaks = c(0,0.05, 0.1, 0.5, 1)) +
theme_Publication()
# which genes?
# filter(!is.na(GOs)) %>%
# view()
## Check these genes for other annotations (signalP, SSP, TMHMM)
# LIST ONE - Cluster identity
list1 <- cluster.dat
names(list1) <- names(cluster.dat)
## LIST TWO - ophio rhythmic genes
signalP <- ocflo.annots %>% filter(signalP == "yes") %>% pull(gene_name)
SSP <- ocflo.annots %>% filter(SSP == "yes") %>% pull(gene_name)
TMHMM <- ocflo.annots %>% filter(TMHMM == "yes") %>% pull(gene_name)
list2 <- list(signalP, SSP, TMHMM)
names(list2) <- paste0(sample.name[[2]], "-", c("signalP", "SSP", "TMHMM"))
## CHECK FOR OVERLAP
library(GeneOverlap)
# define the background geneset
# in our case, it would be the number of orthologous genes between beau and Ophio_cflo
nGenes = ocflo.annots %>% nrow()
## make a GOM object
gom <- newGOM(list1, list2, genome.size = nGenes)
png(paste0(path_to_repo, "/results/figures/BD/ocflo_beau_orthologs_annots_overlap.png"),
width = 15, height = 15, units = "cm", res = 300)
drawHeatmap(gom,
adj.p=T,
cutoff=0.01,
what="odds.ratio",
# what="Jaccard",
log.scale = T,
note.col = "grey60")
trash <- dev.off()
Overlap of orthologous rhy24 gene clusters with additional annotations
4. Ophio DEGs during infection
Do prep for analyses
Prepare the functions, libraries required
# Let's load functions for running limorhyde
source(system.file('extdata', 'vignette_functions.R', package = 'limorhyde'))
# Let's load the libraries required for running Limorhyde
# library('annotate')
library('data.table')
library('foreach')
# library('GEOquery')
library('ggplot2')
library('knitr')
library('limma')
library('limorhyde')
conflict_prefer("union", "dplyr")Format metadata
Create dataframe with metadata information for the different samples collected
sampleName <- c("ophio_cflo","ophio_ophio-infected")
short.name <- c("AC","AI") # AC = arb2-control, AI = arb2-infection
time.points <- c(2,4,6,8,10,12,14,16,18,20,22,24)
light.dark <- c(rep("light",times=5), rep("dark",times=6), rep("light", times=1))
meta <- data.frame(title = paste0(rep(sampleName, each=12),"_ZT",time.points),
sample = paste0(rep(time.points, times=2),rep(short.name, each=12)),
genotype = rep(sampleName, each=12),
time = rep(time.points, times=2),
cond = rep(sampleName, each=12),
LD = rep(light.dark, times=2),
stringsAsFactors = F)
meta %>% glimpse()## Observations: 24
## Variables: 6
## $ title <chr> "ophio_cflo_ZT2", "ophio_cflo_ZT4", "ophio_cflo_ZT6", "ophio…
## $ sample <chr> "2AC", "4AC", "6AC", "8AC", "10AC", "12AC", "14AC", "16AC", …
## $ genotype <chr> "ophio_cflo", "ophio_cflo", "ophio_cflo", "ophio_cflo", "oph…
## $ time <dbl> 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 2, 4, 6, 8, 10, …
## $ cond <chr> "ophio_cflo", "ophio_cflo", "ophio_cflo", "ophio_cflo", "oph…
## $ LD <chr> "light", "light", "light", "light", "light", "dark", "dark",…Now, format the metadata.
### 1.1.1 Format the meta-data ----------------
# load the meta-data
sm <- meta
# Let's format the columns in the right data-type
sm$time <- as.numeric(sm$time)
# sm$batch <- as.factor(sm$batch)
sm$LD <- as.factor(sm$LD)
# sm$location <- as.factor(sm$location)
# Let's get a glimpse of the metadata
sm %>% as_tibble() %>% head()## # A tibble: 6 x 6
## title sample genotype time cond LD
## <chr> <chr> <chr> <dbl> <chr> <fct>
## 1 ophio_cflo_ZT2 2AC ophio_cflo 2 ophio_cflo light
## 2 ophio_cflo_ZT4 4AC ophio_cflo 4 ophio_cflo light
## 3 ophio_cflo_ZT6 6AC ophio_cflo 6 ophio_cflo light
## 4 ophio_cflo_ZT8 8AC ophio_cflo 8 ophio_cflo light
## 5 ophio_cflo_ZT10 10AC ophio_cflo 10 ophio_cflo light
## 6 ophio_cflo_ZT12 12AC ophio_cflo 12 ophio_cflo dark# Next we use limorhyde to calculate time_cos and time_sin, which are based on the first
#harmonic of a Fourier decomposition of the time column, and append them to the sm data frame.
sm = cbind(sm, limorhyde(sm$time, 'time_'))
# convert the dataframe into a data.table
sm <- data.table(sm)
# check that it worked
sm[1:5, ]## title sample genotype time cond LD time_cos
## 1: ophio_cflo_ZT2 2AC ophio_cflo 2 ophio_cflo light 8.660254e-01
## 2: ophio_cflo_ZT4 4AC ophio_cflo 4 ophio_cflo light 5.000000e-01
## 3: ophio_cflo_ZT6 6AC ophio_cflo 6 ophio_cflo light 6.123234e-17
## 4: ophio_cflo_ZT8 8AC ophio_cflo 8 ophio_cflo light -5.000000e-01
## 5: ophio_cflo_ZT10 10AC ophio_cflo 10 ophio_cflo light -8.660254e-01
## time_sin
## 1: 0.5000000
## 2: 0.8660254
## 3: 1.0000000
## 4: 0.8660254
## 5: 0.5000000load data
## DATASET 1
## Load the control O.cflo data (from TC6)
ocflo.control.dat <-
data.db %>%
tbl(., paste0(sampleName[[1]], "_fpkm")) %>%
select(gene_name, everything()) %>%
collect()
## DATASET 2
## Load the O.cflo infection data from the mixed transcriptomics study (from TC7)
inf.db <- dbConnect(RSQLite::SQLite(),
paste0(path_to_repo,"/../Das_et_al_2022b/data/databases/TC7_data.db"))
# src_dbi(inf.db)
# extract the (gene-expr X time-point) data
ocflo.inf.dat <-
inf.db %>%
tbl(., paste0(sampleName[[2]], "_fpkm")) %>%
select(gene_name, everything()) %>%
collect()filter data
The goal is to use only the genes that show expression (>1 FPKM) for at least half of the timepoints during the 24h day (i.e., 6 of the 12 timepoints).
## DATASET 1
n.exp.1 <- apply(ocflo.control.dat[-1], 1, function(x) sum(x>=1))
ocflo.control.dat <- ocflo.control.dat[which(n.exp.1>=6),]
colnames(ocflo.control.dat)[-1] <- paste0("ZT", meta[meta$cond==sampleName[[1]],] %>% pull(sample))
## DATASET 2
n.exp.2 <- apply(ocflo.inf.dat[-1], 1, function(x) sum(x>=1))
ocflo.inf.dat <- ocflo.inf.dat[which(n.exp.2>=6),]
colnames(ocflo.inf.dat)[-1] <- paste0("ZT", meta[meta$cond==sampleName[[2]],] %>% pull(sample))
## Use the genes that are expressed in both conditions
emat <-
ocflo.control.dat %>%
filter(gene_name %in% ocflo.inf.dat$gene_name) %>%
left_join(ocflo.inf.dat, by="gene_name") %>%
as.data.frame()Run analyses
Next, let’s perform the DEG analyses for the ophio-cflo (halfway through infection v. controls)
### Convert to a matrix
# save gene names as row names
rownames(emat) <- emat[,1]
emat <- emat[,-1]
# Need to make the emat into a matrix.
emat <- data.matrix(emat)
# log2 transform the data
emat <- log2(emat + 1)
### Set thresholds
# Set threshold for q-value and log2FC
q.threshold <- 0.05 # currently, using 5% FDR (BH adjusted p-value)
log2.foldchange <- 1 # thus, any gene with a 2^(log2.foldchange) fold change in it's expression
### Format the metadata, if necessary
# Filter the metadata according to your comparison
sm.sub <- sm %>% filter(cond %in% c(sampleName))
# Define the cond column as a factor
sm.sub$cond <- as.factor(sm.sub$cond)
### Let's run the DEG analyses
# Use the subsetted emat to find DEGs
design.deg = model.matrix(~ cond + time_cos + time_sin, data = sm.sub)
#
fit = lmFit(emat, design.deg)
fit = eBayes(fit, trend = TRUE)
# Take a look at the coefficients table
# fit$coefficients %>% head()
#
deLimma.deg = data.table(topTable(fit, coef = 2, number = Inf), keep.rownames = TRUE)
setnames(deLimma.deg, 'rn', 'gene_name')
deLimma.deg[, adj.P.Val := p.adjust(P.Value, method = 'BH')]
setorderv(deLimma.deg, 'adj.P.Val')
### Annotate the results
# Annotate the results to indicate the significant genes
all.DEGs <-
deLimma.deg %>%
arrange(desc(abs(logFC)), adj.P.Val) %>%
mutate(sig = as.factor(ifelse(adj.P.Val < q.threshold & abs(logFC) >= log2.foldchange, "yes", "no"))) %>%
mutate(inf_v_control = as.factor(ifelse(sig=="yes", ifelse( logFC > 0, "up", "down" ), "NA"))) %>%
mutate(inf = sampleName[[2]])
### Summarize the results
writeLines(paste0("\nControl-", sampleName[[1]], " v. ", sampleName[[2]], "\n--Results of DEG analysis--"))##
## Control-ophio_cflo v. ophio_ophio-infected
## --Results of DEG analysis--## How many DEGs - 5% FDR and ≥ 1 fold change in gene expression
all.DEGs %>%
# filter(adj.P.Val < q.threshold) %>%
# filter(abs(logFC) >= 2) %>% # change the criteria here for top DEG or all DEG (logFC≥1)
filter(sig == "yes") %>%
# pull(gene_name) %>%
group_by(inf_v_control) %>%
summarise(n_genes = n()) %>%
as.data.frame() %>%
## n = 81 up- and 141 down-regulated genes in Cflo heads during Ophio-infection
## (at 5% FDR; log2-fold-change ≥ 1)
print()## inf_v_control n_genes
## 1 down 395
## 2 up 318### Subset to keep only sig. DEGs
sig.DEGs <- all.DEGs %>% filter(sig=="yes")Visualize results
# Volcano plot
library(viridis)
ggplot(all.DEGs) +
# geom_hline(yintercept = -log10(0.05), col="red", alpha=0.6) +
# geom_vline(xintercept = c(-2,2), col="grey60", alpha=0.75) +
geom_point(aes(x = logFC, y = -log10(adj.P.Val), color=sig), size = 1.5, alpha = 0.5) +
labs(x = expression(log[2]*' fold-change (inf_v_control)'),
y = expression(-log[10]*' '*q[DE]),
title = "O.cflo (infection v. control)",
color = "significant") +
# scale_x_continuous(limits = c(-5,3),
# breaks = c(-5,-4,-3,-2,-1,0,1,2,3),
# labels = c("-5","","-3","","-1","","1","","3")) +
# xlim(c(-50,50)) +
theme_Publication() +
scale_color_viridis(discrete = T, direction = -1, option = "viridis")
Load manip data
## Load the ophio DEG (at manipulation) data from Will et al. 2020
will2020_data <- read.csv(paste0(path_to_repo,
"/data/input/ophio_cflo/complete_annotations/FullBlast_EC05_RNAseq_orignal_copy_26Aug19.csv"),
stringsAsFactors = F)
will2020_data %<>%
as_tibble() %>%
filter(sample_1=="Alive" & sample_2=="Fungus") %>%
select(arb2_gene, logFC = log2.fold_change., q_value, significant) %>%
mutate(logFC=as.numeric(logFC), q_value=as.numeric(q_value)) %>%
filter(significant=="yes") %>%
mutate(up_down = ifelse(logFC > 0, "down", "up")) %>%
mutate(logFC = -1*logFC) %>%
na.omit()
### Change ophio gene names to ncbi IDs
will2020_data %<>%
left_join(ocflo.annots[1:3], by=c("arb2_gene"="gene_ID_robin")) %>%
select(-1) %>%
select(gene_name, blast_annot, everything())5. Overlap between DEGs during infection v. manipulation
### Subset the up/down-regulated genes
### At halfway-through disease progression
inf.up <- sig.DEGs %>% filter(inf_v_control=="up") %>% pull(gene_name)
inf.down <- sig.DEGs %>% filter(inf_v_control=="down") %>% pull(gene_name)
### At active manipulation
manip.up <- will2020_data %>% filter(up_down=="up") %>% pull(gene_name)
manip.down <- will2020_data %>% filter(up_down=="down") %>% pull(gene_name)
### Visualize the results
listInput <- list(inf.up, inf.down, manip.up, manip.down)
names(listInput) <- c(paste0("inf_",c("up","down")), paste0("manip_", c("up","down")))
library(UpSetR)
library(viridis)
upset(fromList(listInput),
number.angles = 0, point.size = 3, line.size = 1.5,
mainbar.y.label = "Number of overlapping genes",
sets.x.label = "Sig. DE genes",
text.scale = c(1.5, # y-axis label ("# overlapping genes")
2, # y-axis tick labels ("1000, 2000,..")
1.5, # label for histogram ("sig. rhy genes")
1, # tick labels for histogram
1.5, # set names ("Cflo-brain_08h,..")
1.5),
sets = names(listInput),
nintersects = 15,
keep.order = T,
sets.bar.color = viridis(1),
# adding queries
query.legend = "bottom"
) %>%
print()
### Test significance of overlap
list1 <- list(inf.up, inf.down)
names(list1) <- paste0("inf_",c("up","down"))
list2 <- list(manip.up, manip.down)
names(list2) <- paste0("manip_", c("up","down"))
bg.genes <- all.DEGs %>% nrow()
overlap <- check_overlap(list1, list2, bg.genes)
## $rowInd
## [1] 2 1
##
## $colInd
## [1] 1 2
##
## $call
## heatmap.2(x = plot.mat, Rowv = NA, Colv = NA, dendrogram = "none",
## scale = "none", col = brewer.pal(ncolused, grid.col), colsep = col_sep,
## rowsep = row_sep, sepcolor = "white", sepwidth = c(0.002,
## 0.002), cellnote = note.mat, notecex = 1.6, notecol = note.col,
## trace = "none", margins = margins_use, cexRow = row_cexrc,
## cexCol = col_cexrc, key = T, keysize = key_size, density.info = "none",
## main = main.txt, xlab = footnote)
##
## $carpet
## inf_down inf_up
## manip_up 0.00000 2.075416
## manip_down 1.20292 0.000000
##
## $rowDendrogram
## 'dendrogram' with 2 branches and 2 members total, at height 1.414214
##
## $colDendrogram
## 'dendrogram' with 2 branches and 2 members total, at height 1.414214
##
## $breaks
## [1] 0.0000000 0.2306017 0.4612035 0.6918052 0.9224070 1.1530087 1.3836105
## [8] 1.6142122 1.8448140 2.0754157
##
## $col
## [1] "#F7FCF5" "#E5F5E0" "#C7E9C0" "#A1D99B" "#74C476" "#41AB5D" "#238B45"
## [8] "#006D2C" "#00441B"
##
## $colorTable
## low high color
## 1 0.0000000 0.2306017 #F7FCF5
## 2 0.2306017 0.4612035 #E5F5E0
## 3 0.4612035 0.6918052 #C7E9C0
## 4 0.6918052 0.9224070 #A1D99B
## 5 0.9224070 1.1530087 #74C476
## 6 1.1530087 1.3836105 #41AB5D
## 7 1.3836105 1.6142122 #238B45
## 8 1.6142122 1.8448140 #006D2C
## 9 1.8448140 2.0754157 #00441B
##
## $layout
## $layout$lmat
## [,1] [,2]
## [1,] 4 3
## [2,] 2 1
##
## $layout$lhei
## [1] 1.485097 4.000000
##
## $layout$lwid
## [1] 1.485097 4.0000006. Daily exp (DEGs at inf)
Let’s plot the daily expression of the DEGs (ocflo controls v. during infection)
# inf.down %>%
# # stacked.zplot_tc6(cond = "inf")
# stacked.zplot_tc6(cond = "ophio")Clustering of DEGs
- perform hierarchical clustering of DE genes into four clusters;
- plot time-course heatmaps for the clustered 24h-rhythmic geneset
- Identify the day-peaking and night-peaking clusters visually.
## Get the ocflo infection timecourse data (zscores)
zscore.inf.dat <- inf.db %>% tbl(., paste0(sampleName[[2]], "_zscores")) %>% collect()
colnames(zscore.inf.dat)[-1] <- paste0("ZT",meta[meta$cond==sampleName[[2]],] %>% pull(sample))
## Specify parameters
n_clusters <- 4
which.degs <- list(inf.up, inf.down, manip.up, manip.down)
names(which.degs) <- c("inf.up", "inf.down", "manip.up", "manip.down")
for (i in 1:length(which.degs)) {
## Which genes to look at?
# which.genes <- c(inf.up,inf.down)
which.genes <- which.degs[[i]]
### Make the dataframe for plotting
zscore.deg.dat <-
zscore.dat[[2]] %>%
filter(gene_name %in% which.genes) %>%
# add data from infection
left_join(zscore.inf.dat, by="gene_name") %>%
# drop any genes without expression values (NA)
na.omit() %>%
as.data.frame() %>%
# set genes as rownames
column_to_rownames("gene_name")
# Set genes as rownames and convert it into a matrix
# rownames(zscore.rhy.homology.dat) = zscore.rhy.homology.dat$gene_name
zscore.deg.dat <- as.matrix(zscore.deg.dat)
# Hierarchical clustering of the genesets
my_hclust_gene <- hclust(dist(zscore.deg.dat), method = "complete")
# Make annotations for the heatmaps
my_clusters <- cutree(tree = as.dendrogram(my_hclust_gene), k = n_clusters) # k= number of clusters
my_gene_col <- data.frame(cluster = my_clusters)
# I’ll add some column annotations and create the heatmap.
# Annotations for:
# 1. Is the sample collected during the light or dark phase?
my_sample_col <- data.frame(phase = rep(rep(c("light", "dark", "light"),c(5,6,1)),2),
conds = rep(c("ocflo_controls", "ocflo_infection"), each=12))
row.names(my_sample_col) <- colnames(zscore.deg.dat)
# Manual color palette
my_colour = list(
phase = c(light = "#F2E18D", dark = "#010440"),
conds = c(ocflo_controls = col.scheme[[2]], ocflo_infection = col.scheme[[4]]),
cluster = viridis::cividis(100)[c(seq(10,80,by=round(80/(n_clusters), 0)))])
# Color scale
my.breaks = seq(min(zscore.deg.dat), max(zscore.deg.dat), by=0.1)
# my.breaks = seq(min(zscore.rhy), max(zscore.rhy), by=0.06)
# Let's plot!
pheatmap(zscore.deg.dat, show_rownames = F, show_colnames = F,
annotation_row = my_gene_col,
annotation_col = my_sample_col,
cutree_rows = n_clusters, # OG was 4
# cutree_cols = 2,
gaps_col = c(12,24),
annotation_colors = my_colour,
border_color=FALSE,
cluster_cols = F,
breaks = my.breaks,
## color scheme borrowed from:
color = inferno(length(my.breaks) - 1),
treeheight_row = 0,
# treeheight_col = 0,
# remove the color scale or not
main = paste0("DEGs - ",names(which.degs)[[i]], "\n (n=",nrow(zscore.deg.dat), " genes)"),
## annotation legend
annotation_legend = T,
## Color scale
legend = T) %>%
print()
for (j in 1:n_clusters){
writeLines(paste0("Which DEGs: ", names(which.degs)[[i]], "\n", "Cluster: ", j))
# Summary
genes <- my_gene_col %>% rownames_to_column("g") %>% filter(cluster==as.character(j)) %>% pull(g)
writeLines(paste0("n(genes) = ", length(genes),"\n"))
# define the background geneset for enrichment analysis
bg.genes <- all.DEGs %>% pull(gene_name)
# Enrichment
overrepresented.terms <-
genes %>%
check_enrichment(.,
function.dir = path_to_repo,
what = "pfams",
org = sampleName[[1]],
bg = bg.genes,
filter = T,
plot = F)
writeLines(paste0("\n", "n(overrepresented terms) = ", nrow(overrepresented.terms), "\n"))
# Stacked zplot
stacked.plot1 <- genes %>% stacked.zplot_tc6(cond = sampleName[[1]]) %>% pluck(1)
stacked.plot2 <- genes %>% stacked.zplot_tc6(cond = sampleName[[2]]) %>% pluck(1)
ggpubr::ggarrange(plotlist=list(stacked.plot1, stacked.plot2),
nrow = 1, ncol = 2,
widths = c(1,1), labels = NA) %>%
print()
}
}
## Which DEGs: inf.up
## Cluster: 1
## n(genes) = 230
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 4
## Which DEGs: inf.up
## Cluster: 2
## n(genes) = 44
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
##
## n(overrepresented terms) =
## Which DEGs: inf.up
## Cluster: 3
## n(genes) = 34
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
##
## n(overrepresented terms) =
## Which DEGs: inf.up
## Cluster: 4
## n(genes) = 10
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
##
## n(overrepresented terms) =
## Which DEGs: inf.down
## Cluster: 1
## n(genes) = 79
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
##
## n(overrepresented terms) =
## Which DEGs: inf.down
## Cluster: 2
## n(genes) = 169
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 3
## Which DEGs: inf.down
## Cluster: 3
## n(genes) = 103
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
##
## n(overrepresented terms) =
## Which DEGs: inf.down
## Cluster: 4
## n(genes) = 44
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
##
## n(overrepresented terms) =
## Which DEGs: manip.up
## Cluster: 1
## n(genes) = 275
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 3
## Which DEGs: manip.up
## Cluster: 2
## n(genes) = 432
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 9
## Which DEGs: manip.up
## Cluster: 3
## n(genes) = 788
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 8
## Which DEGs: manip.up
## Cluster: 4
## n(genes) = 368
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 4
## Which DEGs: manip.down
## Cluster: 1
## n(genes) = 244
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 2
## Which DEGs: manip.down
## Cluster: 2
## n(genes) = 420
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 3
## Which DEGs: manip.down
## Cluster: 3
## n(genes) = 371
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 2
## Which DEGs: manip.down
## Cluster: 4
## n(genes) = 588
##
## [1] "Loading annotation file for Ophiocordyceps camponoti-floridani"
## [1] "Done."
## [1] "Testing for enrichment..."
##
## n(overrepresented terms) = 5